Independent learning (IL) decomposes an \(n\) -agent MARL problem into \(n\)  decentralised single-agent problems where all other agents are treated as part of the environment, and learning policies that condition only on an agent's local observation history.
While easy to distribute and decentralisable by construction, IL suffers from a variety of theoretical limitations that may result in learning instabilities or suboptimal performance observed in practice [1]}, [2]}.
Firstly, the presence of other learning and exploring agents renders the resulting environment non-stationary from the given agent's perspective, forfeiting convergence guarantees [1]}.
Secondly, independent learners are not always able to distinguish environment stochasticity from another agent's exploration, making them unable to learn optimal policies in some environments [4]}.
